## install.packages("tidytuesdayR")
library(tidyverse)
library(janitor)
library(forcats)
library(dplyr)
library(patchwork)Q2_code
tuesdata <- tidytuesdayR::tt_load('2025-01-21')
#Although our two questions only use exped_tidy, we have added code to make a separate peaks_tidy in case we find out that we need variables from that dataframe later on.
exped_tidy <- tuesdata$exped_tidy
#peaks_tidy <- tuesdata$peaks_tidy
exped_tidy <- clean_names(exped_tidy)
#peaks_tidy <- clean_names(peaks_tidy)
head(exped_tidy, n=10)# A tibble: 10 × 69
expid peakid year season season_factor host host_factor route1 route2
<chr> <chr> <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr>
1 EVER20101 EVER 2020 1 Spring 2 China N Col-N… <NA>
2 EVER20102 EVER 2020 1 Spring 2 China N Col-N… <NA>
3 EVER20103 EVER 2020 1 Spring 2 China N Col-N… <NA>
4 AMAD20301 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
5 AMAD20302 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
6 AMAD20303 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
7 AMAD20304 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
8 AMAD20305 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
9 AMAD20306 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
10 AMAD20307 AMAD 2020 3 Autumn 1 Nepal SW Ridge <NA>
# ℹ 60 more variables: route3 <lgl>, route4 <lgl>, nation <chr>, leaders <chr>,
# sponsor <chr>, success1 <lgl>, success2 <lgl>, success3 <lgl>,
# success4 <lgl>, ascent1 <chr>, ascent2 <chr>, ascent3 <lgl>, ascent4 <lgl>,
# claimed <lgl>, disputed <lgl>, countries <chr>, approach <chr>,
# bcdate <date>, smtdate <date>, smttime <chr>, smtdays <dbl>, totdays <dbl>,
# termdate <date>, termreason <dbl>, termreason_factor <chr>, termnote <chr>,
# highpoint <dbl>, traverse <lgl>, ski <lgl>, parapente <lgl>, camps <dbl>, …#head(peaks_tidy, n=10)exped_tidy <- exped_tidy |>
#mutate new columns
mutate(
"pmdeaths" = mdeaths/totmembers,
"phdeaths" = hdeaths/tothired,
"totdeaths" = (hdeaths+ mdeaths),
"ptotdeaths" = totdeaths/(tothired + totmembers)
)
#filter for all epeditions with at least one death
exped_tidy_deadly <- exped_tidy |>
filter(pmdeaths > 0 | phdeaths > 0) |>
filter(!is.na(agency))
#create a subset dataframe that clears out unecessary columns
exped_tidy_deadly <- exped_tidy_deadly |>
select(year, season_factor, host_factor, nation, agency, totmembers, smtmembers, mdeaths, tothired, hdeaths, totdeaths, pmdeaths, phdeaths, ptotdeaths)
exped_tidy_deadly# A tibble: 37 × 14
year season_factor host_factor nation agency totmembers smtmembers mdeaths
<dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 2021 Spring Nepal USA TAGnepa… 35 10 0
2 2021 Spring Nepal Russia 7 Summi… 15 7 0
3 2021 Spring Nepal India Seven S… 37 22 2
4 2021 Spring Nepal China Seven S… 13 9 0
5 2021 Autumn Nepal Nepal TAGnepa… 5 0 1
6 2021 Autumn Nepal France Pralhad… 4 0 3
7 2022 Spring Nepal Greece Seven S… 7 3 1
8 2022 Spring Nepal USA Beyul A… 10 8 0
9 2022 Spring Nepal Russia 7 Summi… 15 12 1
10 2022 Spring Nepal Nepal High Fi… 7 6 1
# ℹ 27 more rows
# ℹ 6 more variables: tothired <dbl>, hdeaths <dbl>, totdeaths <dbl>,
# pmdeaths <dbl>, phdeaths <dbl>, ptotdeaths <dbl>#assigns deadly agencies to one tibble
deadly_agencies <- unique(exped_tidy_deadly$agency)
#assigns non-deadly agencies to one tibble
non_deadly <- exped_tidy |>
filter(!agency %in% deadly_agencies)
non_deadly_agencies <- unique(non_deadly$agency)
#prints out the length for each so that I can view these values. Will not be used for graphing
length(non_deadly_agencies)[1] 160length(deadly_agencies)[1] 22exped_tidy_deadly |>
#graphs agency by number of fatal expeditions. fct_infreq was debugged consulting with AI after looking at documentation. Same with after_stat(count)
ggplot(aes(x = fct_rev(fct_infreq(agency)), fill = after_stat(count))) +
geom_bar() +
#flips coordinates for better readbility of agencies
coord_flip() +
#Increased number of breaks
scale_y_continuous(breaks = c(0, 2, 4, 6, 8, 10)) +
#colored where red is more deadly. I wanted a low intensity representing an increasing intensity so I settled on yellow
scale_fill_gradient(low = "#ffce00", high = "darkred") +
labs(
title = "Number of expeditions through the Himalayas \nthat resulted in death by Agency",
subtitle = "from 2021 - 2024",
caption = "Source: Tidytuesday",
x = NULL,
y = "Number of expeditions that resulted in at least one death",
fill = NULL
) +
theme_minimal() +
#got rid of grid to improve readability
theme(
legend.position = "none",
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank()
)
#filtered for only 2021 data
deaths_2021 <- exped_tidy_deadly |>
filter(year == 2021)
deaths_2021# A tibble: 6 × 14
year season_factor host_factor nation agency totmembers smtmembers mdeaths
<dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 2021 Spring Nepal USA TAGnepal… 35 10 0
2 2021 Spring Nepal Russia 7 Summit… 15 7 0
3 2021 Spring Nepal India Seven Su… 37 22 2
4 2021 Spring Nepal China Seven Su… 13 9 0
5 2021 Autumn Nepal Nepal TAGnepal… 5 0 1
6 2021 Autumn Nepal France Pralhad … 4 0 3
# ℹ 6 more variables: tothired <dbl>, hdeaths <dbl>, totdeaths <dbl>,
# pmdeaths <dbl>, phdeaths <dbl>, ptotdeaths <dbl>#grouped to create the average percent deaths per agency
deaths_2021_av <- deaths_2021 %>%
#grouped by season for color reasons
group_by(agency, season_factor) %>%
#we used the average percent for the x axis to better compare between groups
summarise(avg_ptotdeaths = mean(ptotdeaths, na.rm = TRUE),
.groups = "drop")
deaths_2021_av# A tibble: 5 × 3
agency season_factor avg_ptotdeaths
<chr> <chr> <dbl>
1 7 Summits Adventure Spring 0.0222
2 Pralhad Chapagain (Freelancer at Expes.com) Autumn 0.75
3 Seven Summit Treks Spring 0.0356
4 TAGnepal Trekking Spring 0.0118
5 TAGnepal Trekking (Snowy Horizon pmt) Autumn 0.111 #raw death values, similar approach as above
deaths_2021_raw <- deaths_2021 |>
group_by(agency, season_factor) %>%
summarize(total_deaths = sum(totdeaths), .groups = "drop")
deaths_2021_raw# A tibble: 5 × 3
agency season_factor total_deaths
<chr> <chr> <dbl>
1 7 Summits Adventure Spring 1
2 Pralhad Chapagain (Freelancer at Expes.com) Autumn 3
3 Seven Summit Treks Spring 3
4 TAGnepal Trekking Spring 1
5 TAGnepal Trekking (Snowy Horizon pmt) Autumn 1# label: 2021 percent death graph
deaths_2021_av |>
#graph by descending percent total deaths by agency. Color is for season
ggplot(aes(x = fct_reorder(agency, avg_ptotdeaths, .desc = FALSE), y = avg_ptotdeaths, fill = season_factor)) +
geom_col() +
#set coordinates for better comparison between groups
coord_flip(ylim = c(0, 1)) +
#colored based on majority season color association
scale_fill_manual(values = c("orange", "lightgreen")) +
labs(
fill = "Season",
x = "Trekking Agency",
y = "Total Death",
title = "Percent total deaths by Agency in 2021",
caption = "Source: TidyTuesday",
subtitle = "M is trekking member death, H is hired staff death"
) +
#rename x values to have percent
scale_y_continuous(
breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1),
labels = c("0%", "20%", "40%", "60%", "80%", "100%")
) +
#individual labels for each individual percent death rather than average and distingish it between member and hired staff
annotate("text", y = 0.2, x = 1, label = "M 6.6%") +
annotate("text", y = 0.2, x = 2, label = "H 3.3%") +
annotate("text", y = 0.3, x = 3, label = "Trek 1: M 54%") +
annotate("text", y = 0.7, x = 3, label = "Trek 2: H 10%") +
annotate("text", y = 0.3, x = 4, label = "M 20%") +
annotate("text", y = 0.4, x = 5, label = "M 75%") +
theme_minimal() +
#cleaned up grid for better readability of annotations
theme(
axis.ticks.x = element_blank(),
panel.grid = element_blank()
)deaths_2021_raw |>
#same as above, except with total deaths and not percents
ggplot(aes(x = fct_reorder(agency, total_deaths, .desc = FALSE), y = total_deaths, fill = season_factor)) +
geom_col() +
#sets coordinates for easier comparison between groups
coord_flip(ylim = c(0, 5)) +
#colored same as above for easy comparison
scale_fill_manual(values = c("orange", "lightgreen")) +
labs(
fill = "Season",
x = "Trekking Agency",
y = "Total Deaths",
title = "Percent total deaths by Agency in 2021",
caption = "Source: TidyTuesday",
subtitle = "M is trekking member death, H is hired staff death"
) +
#set breaks
scale_y_continuous(
breaks = c(0, 1, 2, 3, 4, 5)
) +
#will not use this annotation after all
#annotate("text", y = 2, x = 3, label = "M 1") +
#annotate("text", y = 2, x = 2, label = "H 1") +
#annotate("text", y = 2, x = 1, label = "Trek 1: M 2") +
#annotate("text", y = 4, x = 1, label = "Trek 2: H 1") +
#annotate("text", y = 4, x = 4, label = "M 3") +
#annotate("text", y = 4, x = 5, label = "M 3") +
#instead annotate with number of treks because most deaths were 1 or 2 total so the prior labels are somewhat superfulous and messy
annotate("text", y = 4, x = 5, label = "Total Treks: 2") +
theme_minimal() +
#clean grid for better readability
theme(
axis.ticks.x = element_blank(),
panel.grid = element_blank()
)
All code below are copy and pasted of the graphs above witht the year and specific annotations changed.
deaths_2022 <- exped_tidy_deadly |>
filter(year == 2022)
deaths_2022# A tibble: 10 × 14
year season_factor host_factor nation agency totmembers smtmembers mdeaths
<dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 2022 Spring Nepal Greece Seven… 7 3 1
2 2022 Spring Nepal USA Beyul… 10 8 0
3 2022 Spring Nepal Russia 7 Sum… 15 12 1
4 2022 Spring Nepal Nepal High … 7 6 1
5 2022 Spring Nepal Nepal Pione… 13 9 1
6 2022 Spring Nepal S Korea Seven… 8 0 0
7 2022 Autumn Nepal Georgia Sator… 16 12 1
8 2022 Autumn Nepal Netherl… Seven… 15 0 0
9 2022 Autumn Nepal USA Shang… 5 2 1
10 2022 Autumn Nepal USA Sator… 10 0 0
# ℹ 6 more variables: tothired <dbl>, hdeaths <dbl>, totdeaths <dbl>,
# pmdeaths <dbl>, phdeaths <dbl>, ptotdeaths <dbl>deaths_2022_av <- deaths_2022 %>%
group_by(agency, season_factor) %>%
summarise(avg_ptotdeaths = mean(ptotdeaths, na.rm = TRUE),
.groups = "drop")
deaths_2022_av# A tibble: 8 × 3
agency season_factor avg_ptotdeaths
<chr> <chr> <dbl>
1 7 Summits Adventure Spring 0.0263
2 Beyul Adventure Spring 0.0323
3 High Five Adventures (Pioneer Adventures pmt) Spring 0.1
4 Pioneer Adventure Spring 0.0333
5 Satori Adventures Autumn 0.0406
6 Seven Summit Treks Autumn 0.0333
7 Seven Summit Treks Spring 0.0565
8 Shangri-La Nepal Treks Autumn 0.0833deaths_2022_raw <- deaths_2022 |>
group_by(agency, season_factor) %>%
summarize(total_deaths = sum(totdeaths), .groups = "drop")
deaths_2022_raw# A tibble: 8 × 3
agency season_factor total_deaths
<chr> <chr> <dbl>
1 7 Summits Adventure Spring 1
2 Beyul Adventure Spring 1
3 High Five Adventures (Pioneer Adventures pmt) Spring 1
4 Pioneer Adventure Spring 1
5 Satori Adventures Autumn 2
6 Seven Summit Treks Autumn 1
7 Seven Summit Treks Spring 2
8 Shangri-La Nepal Treks Autumn 1deaths_2022_av |>
ggplot(aes(x = fct_reorder(agency, avg_ptotdeaths, .desc = FALSE), y = avg_ptotdeaths, fill = season_factor)) +
geom_col() +
coord_flip(ylim = c(0, 1)) +
scale_fill_manual(values = c("orange", "lightgreen")) +
labs(
fill = "Season",
x = "Trekking Agency",
y = "Total Percent Death (Average)",
title = "Percent total deaths by Agency in 2022",
caption = "Source: TidyTuesday",
subtitle = "M is trekking member death, H is hired staff death"
) +
scale_y_continuous(
breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1),
labels = c("0%", "20%", "40%", "60%", "80%", "100%")
) +
annotate("text", y = 0.2, x = 1, label = "H 20%") +
annotate("text", y = 0.2, x = 2, label = "H 4.7%") +
annotate("text", y = 0.2, x = 3, label = "M 7.6%") +
annotate("text", y = 0.3, x = 4, label = "Trek 1: M 6.25%") +
annotate("text", y = 0.7, x = 4, label = "Trek 2: H 10%") +
annotate("text", y = 0.32, x = 4.8, label = "Trek 1: M 14.2%") +
annotate("text", y = 0.32, x = 5.2, label = "Trek 2: H 6.25%") +
annotate("text", y = 0.8, x = 5, label = "Trek 3: H 6.67%") +
annotate("text", y = 0.25, x = 6, label = "M 20%") +
annotate("text", y = 0.3, x = 7, label = "M 14.28%") +
theme_minimal() +
theme(
axis.ticks.x = element_blank(),
panel.grid = element_blank()
)
deaths_2022_raw |>
ggplot(aes(x = fct_reorder(agency, total_deaths, .desc = FALSE), y = total_deaths, fill = season_factor)) +
geom_col() +
coord_flip(ylim = c(0, 5)) +
scale_fill_manual(values = c("orange", "lightgreen")) +
labs(
fill = "Season",
x = "Trekking Agency",
y = "Total Percent Death (Average)",
title = "Percent total deaths by Agency in 2022",
caption = "Source: TidyTuesday",
subtitle = "M is trekking member death, H is hired staff death"
) +
scale_y_continuous(
breaks = c(0, 1, 2, 3, 4, 5)
) +
#annotate("text", y = 2, x = 1, label = "M 1") +
#annotate("text", y = 2, x = 2, label = "H 1") +
#annotate("text", y = 2, x = 3, label = "M 1") +
#annotate("text", y = 2, x = 4, label = "M 1") +
#annotate("text", y = 2, x = 5, label = "M 1") +
#annotate("text", y = 1, x = 6, label = "Trek 1: M 1") +
#annotate("text", y = 2.7, x = 6, label = "Trek 2: H 1") +
#annotate("text", y = 4.5, x = 6, label = "Trek 3: H 1") +
#annotate("text", y = 1, x = 7, label = "Trek 1: M 1") +
#annotate("text", y = 3, x = 7, label = "Trek 2: H 1") +
annotate("text", y = 4, x = 6, label = "Total Treks: 3") +
annotate("text", y = 3, x = 7, label = "Total Treks: 2") +
theme_minimal() +
theme(
axis.ticks.x = element_blank(),
panel.grid = element_blank()
)
deaths_2023 <- exped_tidy_deadly |>
filter(year == 2023)
deaths_2023# A tibble: 15 × 14
year season_factor host_factor nation agency totmembers smtmembers mdeaths
<dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 2023 Spring Nepal Pakistan Seven… 21 12 1
2 2023 Spring Nepal Nepal Imagi… 15 10 0
3 2023 Spring Nepal USA Beyul… 6 3 1
4 2023 Spring Nepal USA Himal… 19 12 1
5 2023 Spring Nepal Nepal Asian… 13 10 2
6 2023 Spring Nepal China 8K Ex… 17 6 1
7 2023 Spring Nepal Denmark Seven… 30 14 2
8 2023 Spring Nepal India Pione… 15 6 2
9 2023 Spring Nepal Vietnam Exped… 10 3 1
10 2023 Spring Nepal France Glaci… 5 1 1
11 2023 Spring Nepal Nepal Annap… 1 0 1
12 2023 Spring Nepal Nepal Peak … 8 5 0
13 2023 Spring Nepal Germany Seven… 16 8 1
14 2023 Autumn Nepal Czech R… 14 Su… 6 2 1
15 2023 Autumn Nepal Russia Himal… 3 0 1
# ℹ 6 more variables: tothired <dbl>, hdeaths <dbl>, totdeaths <dbl>,
# pmdeaths <dbl>, phdeaths <dbl>, ptotdeaths <dbl>deaths_2023_av <- deaths_2023 %>%
group_by(agency, season_factor) %>%
summarise(avg_ptotdeaths = mean(ptotdeaths, na.rm = TRUE),
.groups = "drop")
deaths_2023_av# A tibble: 13 × 3
agency season_factor avg_ptotdeaths
<chr> <chr> <dbl>
1 14 Summits Autumn 0.167
2 8K Expeditions Spring 0.0270
3 Annapurna Treks Spring 1
4 Asian Trekking Spring 0.05
5 Beyul Adventure Spring 0.0476
6 Expedition Himalaya Spring 0.0333
7 Glacier Himalaya Treks Spring 0.1
8 Himalayan Guides Autumn 0.333
9 Himalayan Guides Spring 0.0204
10 Imagine Nepal Spring 0.0667
11 Peak Promotion Spring 0.125
12 Pioneer Adventure Spring 0.0667
13 Seven Summit Treks Spring 0.0281deaths_2023_raw <- deaths_2023 |>
group_by(agency, season_factor) %>%
summarize(total_deaths = sum(totdeaths), .groups = "drop")
deaths_2023_raw# A tibble: 13 × 3
agency season_factor total_deaths
<chr> <chr> <dbl>
1 14 Summits Autumn 1
2 8K Expeditions Spring 1
3 Annapurna Treks Spring 2
4 Asian Trekking Spring 2
5 Beyul Adventure Spring 1
6 Expedition Himalaya Spring 1
7 Glacier Himalaya Treks Spring 1
8 Himalayan Guides Autumn 1
9 Himalayan Guides Spring 1
10 Imagine Nepal Spring 3
11 Peak Promotion Spring 2
12 Pioneer Adventure Spring 2
13 Seven Summit Treks Spring 4deaths_2023_av |>
ggplot(aes(x = fct_reorder(agency, avg_ptotdeaths, .desc = FALSE), y = avg_ptotdeaths, fill = season_factor)) +
geom_col() +
coord_flip(ylim = c(0, 1)) +
scale_fill_manual(values = c("orange", "lightgreen")) +
labs(
fill = "Season",
x = "Trekking Agency",
y = "Total Percent Death (Average)",
title = "Percent total deaths by Agency in 2023",
caption = "Source: TidyTuesday",
subtitle = "M is trekking member death, H is hired staff death"
) +
scale_y_continuous(
breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1),
labels = c("0%", "20%", "40%", "60%", "80%", "100%")
) +
annotate("text", y = 0.2, x = 1, label = "M 5.8%") +
annotate("text", y = 0.2, x = 2, label = "Trek 1: M 4.7%") +
annotate("text", y = 0.5, x = 2, label = "Trek 2: M 6.7%") +
annotate("text", y = 0.8, x = 2, label = "Trek 3: 6.3%") +
annotate("text", y = 0.2, x = 3, label = "M 10%") +
annotate("text", y = 0.2, x = 4, label = "M 1.66%") +
annotate("text", y = 0.2, x = 5, label = "M 15.3%") +
annotate("text", y = 0.2, x = 6, label = "H 10%") +
annotate("text", y = 0.2, x = 7, label = "M 13.3%") +
annotate("text", y = 0.2, x = 8, label = "M 20%") +
annotate("text", y = 0.23, x = 9, label = "H 25%") +
annotate("text", y = 0.25, x = 10, label = "M 16.7%") +
annotate("text", y = 0.5, x = 11, label = "Trek 1: M 5.3%") +
annotate("text", y = 0.8, x = 11, label = "Trek 2: M 33.3%") +
annotate("text", y = 0.5, x = 12, label = "M 100%") +
theme_minimal() +
theme(
axis.ticks.x = element_blank(),
panel.grid = element_blank()
)
deaths_2023_raw |>
ggplot(aes(x = fct_reorder(agency, total_deaths, .desc = FALSE), y = total_deaths, fill = season_factor)) +
geom_col() +
coord_flip(ylim = c(0, 5)) +
scale_fill_manual(values = c("orange", "lightgreen")) +
labs(
fill = "Season",
x = "Trekking Agency",
y = "Total Death",
title = "Total deaths by Agency in 2023",
caption = "Source: TidyTuesday",
subtitle = "M is trekking member death, H is hired staff death"
) +
scale_y_continuous(
breaks = c(0, 1, 2, 3, 4, 5)
) +
annotate("text", y = 3, x = 6, label = "Total Treks: 2") +
annotate("text", y = 3, x = 12, label = "Total Treks: 3") +
theme_minimal() +
theme(
axis.ticks.x = element_blank(),
panel.grid = element_blank()
)
deaths_2024 <- exped_tidy_deadly |>
filter(year == 2024)
deaths_2024# A tibble: 6 × 14
year season_factor host_factor nation agency totmembers smtmembers mdeaths
<dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 2024 Spring Nepal Italy Seven S… 22 12 1
2 2024 Spring Nepal Ukraine 8K Expe… 36 29 4
3 2024 Spring Nepal Nepal Yeti Ad… 2 1 1
4 2024 Spring Nepal UK Makalu … 15 7 1
5 2024 Spring Nepal Nepal Seven S… 27 15 0
6 2024 Spring Nepal France Snowy H… 10 5 1
# ℹ 6 more variables: tothired <dbl>, hdeaths <dbl>, totdeaths <dbl>,
# pmdeaths <dbl>, phdeaths <dbl>, ptotdeaths <dbl>deaths_2024_av <- deaths_2024 %>%
group_by(agency, season_factor) %>%
summarise(avg_ptotdeaths = mean(ptotdeaths, na.rm = TRUE),
.groups = "drop")
deaths_2024_av# A tibble: 5 × 3
agency season_factor avg_ptotdeaths
<chr> <chr> <dbl>
1 8K Expeditions Spring 0.0581
2 Makalu Adventure Spring 0.0333
3 Seven Summit Treks Spring 0.0279
4 Snowy Horizon Treks Spring 0.0370
5 Yeti Adventure Spring 0.333 deaths_2024_raw <- deaths_2024 |>
group_by(agency, season_factor) %>%
summarize(total_deaths = sum(totdeaths), .groups = "drop")
deaths_2024_raw# A tibble: 5 × 3
agency season_factor total_deaths
<chr> <chr> <dbl>
1 8K Expeditions Spring 5
2 Makalu Adventure Spring 1
3 Seven Summit Treks Spring 3
4 Snowy Horizon Treks Spring 1
5 Yeti Adventure Spring 1deaths_2024_av |>
ggplot(aes(x = fct_reorder(agency, avg_ptotdeaths, .desc = FALSE), y = avg_ptotdeaths, fill = season_factor)) +
geom_col() +
coord_flip(ylim = c(0, 1)) +
scale_fill_manual(values = "lightgreen") +
labs(
fill = "Season",
x = "Trekking Agency",
y = "Total Percent Death (Average)",
title = "Percent total deaths by Agency in 2024",
caption = "Source: TidyTuesday",
subtitle = "M is trekking member death, H is hired staff death"
) +
scale_y_continuous(
breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1),
labels = c("0%", "20%", "40%", "60%", "80%", "100%")
) +
annotate("text", y = 0.2, x = 2, label = "M 6.7%") +
annotate("text", y = 0.3, x = 1, label = "Trek 1: M 4.5% H 3.3%") +
annotate("text", y = 0.7, x = 1, label = "Trek 2: H 3.2%") +
annotate("text", y = 0.2, x = 3, label = "M 10%") +
annotate("text", y = 0.2, x = 4, label = "M 11.1% H 2%") +
annotate("text", y = 0.5, x = 5, label = "M 50%") +
theme_minimal() +
theme(
axis.ticks.x = element_blank(),
panel.grid = element_blank()
)
deaths_2024_raw |>
ggplot(aes(x = fct_reorder(agency, total_deaths, .desc = FALSE), y = total_deaths, fill = season_factor)) +
geom_col() +
coord_flip(ylim = c(0, 5)) +
scale_fill_manual(values = c("lightgreen")) +
labs(
fill = "Season",
x = "Trekking Agency",
y = "Total Death",
title = "Total deaths by Agency in 2024",
caption = "Source: TidyTuesday",
subtitle = "M is trekking member death, H is hired staff death"
) +
scale_y_continuous(
breaks = c(0, 1, 2, 3, 4, 5)
) +
annotate("text", y = 4, x = 4, label = "Total Treks: 2") +
theme_minimal() +
theme(
axis.ticks.x = element_blank(),
panel.grid = element_blank()
)
Was not able to use patchwork to combine graphs due to different y values
#combined_plot <- wrap_plots(percent_2021, raw_2021, percent_2022, raw_2022, percent_2023, raw_2023, percent_2024, raw_2024, ncol = 2)
#combined_plot#final_2021 <- (percent_2021 + raw_2021)
#final_2021